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(GA) is a Metaheuristic-based optimization method that addresses difficult optimization issues by simulating biological evolution and the survival of the fittest principle in natural contexts. The genetic algorithm (GA) method is presented and then assessed on a control issue to determine the optimal control structure for a certain time horizon. It is necessary to identify the various control parameters in order to execute the various control rules. This is ambiguous since there is no straightforward method for calculating these parameters for nonlinear systems. The introduction of the Genetic Algorithm, a metaheuristic optimization technique for determining the ideal nonlinear controller parameters, is our contribution. Using a dynamic model of a two-link rigid robot manipulator, the obtained results support the recommended optimal control strategy for the regulation and trajectory tracking problem, which is based on intelligent control and GA.

Keywords

GA; Optimization; Intelligent Control; Metaheuristic; Modelling; Nonlinear

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